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2020 – today
- 2024
[j44]Shoji Toyota, Kenji Fukumizu:
Out-of-Distribution Optimality of Invariant Risk Minimization. Trans. Mach. Learn. Res. 2024 (2024)- [c70]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Optimal Transport for Measures with Noisy Tree Metric. AISTATS 2024: 3115-3123 - [c69]Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato:
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. ICLR 2024 - [c68]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Generalized Sobolev Transport for Probability Measures on a Graph. ICML 2024 - [c67]Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic:
Neural-Kernel Conditional Mean Embeddings. ICML 2024 - [i56]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Generalized Sobolev Transport for Probability Measures on a Graph. CoRR abs/2402.04516 (2024) - [i55]Noboru Isobe, Masanori Koyama, Kohei Hayashi, Kenji Fukumizu:
Extended Flow Matching: a Method of Conditional Generation with Generalized Continuity Equation. CoRR abs/2402.18839 (2024) - [i54]Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic:
Neural-Kernel Conditional Mean Embeddings. CoRR abs/2403.10859 (2024) - [i53]Yuto Tanimoto, Kenji Fukumizu:
State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards. CoRR abs/2403.11520 (2024) - [i52]Kenji Fukumizu, Taiji Suzuki, Noboru Isobe, Kazusato Oko, Masanori Koyama:
Flow matching achieves minimax optimal convergence. CoRR abs/2405.20879 (2024) - [i51]Shunya Minami, Yoshihiro Hayashi, Stephen Wu, Kenji Fukumizu, Hiroki Sugisawa, Masashi Ishii, Isao Kuwajima, Kazuya Shiratori, Ryo Yoshida:
Scaling Law of Sim2Real Transfer Learning in Expanding Computational Materials Databases for Real-World Predictions. CoRR abs/2408.04042 (2024) - 2023
- [c66]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Scalable Unbalanced Sobolev Transport for Measures on a Graph. AISTATS 2023: 8521-8560 - [c65]Yuri Kinoshita, Kenta Oono, Kenji Fukumizu, Yuichi Yoshida, Shin-ichi Maeda:
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network. ICML 2023: 17041-17060 - [c64]Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi, Ryo Yoshida:
Transfer Learning with Affine Model Transformation. NeurIPS 2023 - [i50]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Scalable Unbalanced Sobolev Transport for Measures on a Graph. CoRR abs/2302.12498 (2023) - [i49]Yuri Kinoshita, Kenta Oono, Kenji Fukumizu, Yuichi Yoshida, Shin-ichi Maeda:
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network. CoRR abs/2304.12770 (2023) - [i48]Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato:
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. CoRR abs/2305.18484 (2023) - [i47]Shoji Toyota, Kenji Fukumizu:
Out-of-Distribution Optimality of Invariant Risk Minimization. CoRR abs/2307.11972 (2023) - [i46]Tam Le, Truyen Nguyen, Kenji Fukumizu:
Optimal Transport for Measures with Noisy Tree Metric. CoRR abs/2310.13653 (2023) - 2022
- [j43]Hironori Murase
, Kenji Fukumizu:
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables. IEEE Access 10: 44259-44270 (2022) - [j42]Masaaki Imaizumi, Kenji Fukumizu:
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces. J. Mach. Learn. Res. 23: 111:1-111:54 (2022) - [c63]Pengzhou Abel Wu, Kenji Fukumizu:
$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap. ICLR 2022 - [c62]Takeru Miyato, Masanori Koyama, Kenji Fukumizu:
Unsupervised Learning of Equivariant Structure from Sequences. NeurIPS 2022 - [c61]Shoji Toyota, Kenji Fukumizu:
Invariance Learning based on Label Hierarchy. NeurIPS 2022 - [c60]Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi:
A Scaling Law for Syn2real Transfer: How Much Is Your Pre-training Effective? ECML/PKDD (3) 2022: 477-492 - [i45]Hironori Murase, Kenji Fukumizu:
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables. CoRR abs/2202.10281 (2022) - [i44]Shoji Toyota, Kenji Fukumizu:
Invariance Learning based on Label Hierarchy. CoRR abs/2203.15549 (2022) - [i43]Siddharth Vishwanath, Bharath K. Sriperumbudur, Kenji Fukumizu, Satoshi Kuriki:
Robust Topological Inference in the Presence of Outliers. CoRR abs/2206.01795 (2022) - [i42]Takeru Miyato, Masanori Koyama, Kenji Fukumizu:
Unsupervised Learning of Equivariant Structure from Sequences. CoRR abs/2210.05972 (2022) - [i41]Masanori Koyama, Takeru Miyato, Kenji Fukumizu:
Invariance-adapted decomposition and Lasso-type contrastive learning. CoRR abs/2210.07413 (2022) - [i40]Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi, Ryo Yoshida:
Transfer learning with affine model transformation. CoRR abs/2210.09745 (2022) - 2021
- [j41]Daniel Andrade, Kenji Fukumizu, Yuzuru Okajima:
Convex covariate clustering for classification. Pattern Recognit. Lett. 151: 193-199 (2021) - [c59]Shunya Minami, Song Liu, Stephen Wu, Kenji Fukumizu, Ryo Yoshida:
A General Class of Transfer Learning Regression without Implementation Cost. AAAI 2021: 8992-8999 - [c58]Jean-François Ton, Dino Sejdinovic, Kenji Fukumizu:
Meta Learning for Causal Direction. AAAI 2021: 9897-9905 - [e1]Arindam Banerjee, Kenji Fukumizu:
The 24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021, April 13-15, 2021, Virtual Event. Proceedings of Machine Learning Research 130, PMLR 2021 [contents] - [i39]Pengzhou Wu, Kenji Fukumizu:
Identifying Treatment Effects under Unobserved Confounding by Causal Representation Learning. CoRR abs/2101.06662 (2021) - [i38]Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi:
A Scaling Law for Synthetic-to-Real Transfer: A Measure of Pre-Training. CoRR abs/2108.11018 (2021) - [i37]Pengzhou Wu, Kenji Fukumizu:
Towards Principled Causal Effect Estimation by Deep Identifiable Models. CoRR abs/2109.15062 (2021) - [i36]Pengzhou Wu, Kenji Fukumizu:
β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap. CoRR abs/2110.05225 (2021) - 2020
- [j40]Shaogao Lv, Zengyan Fan, Heng Lian, Taiji Suzuki, Kenji Fukumizu:
A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model. Comput. Stat. Data Anal. 152: 107039 (2020) - [j39]Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu:
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. Found. Comput. Math. 20(1): 155-194 (2020) - [j38]Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu:
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. Mach. Learn. 109(5): 939-972 (2020) - [j37]Daniel Andrade, Akiko Takeda, Kenji Fukumizu:
Robust Bayesian model selection for variable clustering with the Gaussian graphical model. Stat. Comput. 30(2): 351-376 (2020) - [j36]Niko Yasui, Chrysafis Vogiatzis, Ruriko Yoshida, Kenji Fukumizu:
imPhy: Imputing Phylogenetic Trees with Missing Information Using Mathematical Programming. IEEE ACM Trans. Comput. Biol. Bioinform. 17(4): 1222-1230 (2020) - [c57]Pengzhou Wu, Kenji Fukumizu:
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. AISTATS 2020: 1157-1167 - [c56]Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu:
Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning. ECCV (17) 2020: 626-646 - [c55]Casey Chu, Kentaro Minami, Kenji Fukumizu:
Smoothness and Stability in GANs. ICLR 2020 - [c54]Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur:
Robust Persistence Diagrams using Reproducing Kernels. NeurIPS 2020 - [i35]Pengzhou Wu, Kenji Fukumizu:
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. CoRR abs/2001.01894 (2020) - [i34]Casey Chu, Kentaro Minami, Kenji Fukumizu:
Smoothness and Stability in GANs. CoRR abs/2002.04185 (2020) - [i33]Casey Chu, Kentaro Minami, Kenji Fukumizu:
The equivalence between Stein variational gradient descent and black-box variational inference. CoRR abs/2004.01822 (2020) - [i32]Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur:
Robust Persistence Diagrams using Reproducing Kernels. CoRR abs/2006.10012 (2020) - [i31]Shunya Minami, Song Liu, Stephen Wu, Kenji Fukumizu, Ryo Yoshida:
A General Class of Transfer Learning Regression without Implementation Cost. CoRR abs/2006.13228 (2020) - [i30]Jean-Francois Ton, Dino Sejdinovic, Kenji Fukumizu:
Meta Learning for Causal Direction. CoRR abs/2007.02809 (2020) - [i29]Masaaki Imaizumi, Kenji Fukumizu:
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Curves. CoRR abs/2011.02256 (2020)
2010 – 2019
- 2019
- [j35]Ruriko Yoshida, Kenji Fukumizu, Chrysafis Vogiatzis:
Multilocus phylogenetic analysis with gene tree clustering. Ann. Oper. Res. 276(1-2): 293-313 (2019) - [c53]Masaaki Imaizumi, Kenji Fukumizu:
Deep Neural Networks Learn Non-Smooth Functions Effectively. AISTATS 2019: 869-878 - [c52]Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu:
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator. ICLR (Poster) 2019 - [c51]Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi:
Tree-Sliced Variants of Wasserstein Distances. NeurIPS 2019: 12283-12294 - [c50]Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka:
Semi-flat minima and saddle points by embedding neural networks to overparameterization. NeurIPS 2019: 13845-13853 - [i28]Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi:
Tree-Sliced Approximation of Wasserstein Distances. CoRR abs/1902.00342 (2019) - [i27]Takafumi Kajihara, Motonobu Kanagawa, Yuuki Nakaguchi, Kanishka Khandelwal, Kenji Fukumizu:
Model Selection for Simulator-based Statistical Models: A Kernel Approach. CoRR abs/1902.02517 (2019) - [i26]Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka:
Semi-flat minima and saddle points by embedding neural networks to overparameterization. CoRR abs/1906.04868 (2019) - [i25]Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton:
A Kernel Stein Test for Comparing Latent Variable Models. CoRR abs/1907.00586 (2019) - [i24]Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu:
Deep Set-to-Set Matching and Learning. CoRR abs/1910.09972 (2019) - 2018
- [j34]Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang:
Influence function and robust variant of kernel canonical correlation analysis. Neurocomputing 304: 12-29 (2018) - [c49]Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi:
Post Selection Inference with Kernels. AISTATS 2018: 152-160 - [c48]Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu, Jun Suzuki, Kentaro Inui:
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions. EMNLP 2018: 1763-1775 - [c47]Yao-Hung Hubert Tsai, Denny Wu, Makoto Yamada, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu:
Selecting the Best in GANs Family: a Post Selection Inference Framework. ICLR (Workshop) 2018 - [c46]Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu:
Kernel Recursive ABC: Point Estimation with Intractable Likelihood. ICML 2018: 2405-2414 - [c45]Hao Zhang, Shinji Nakadai, Kenji Fukumizu:
From Black-Box to White-Box: Interpretable Learning with Kernel Machines. MLDM (1) 2018: 213-227 - [c44]Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu:
Variational Learning on Aggregate Outputs with Gaussian Processes. NeurIPS 2018: 6084-6094 - [i23]Yao-Hung Hubert Tsai, Makoto Yamada, Denny Wu, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu:
Selecting the Best in GANs Family: a Post Selection Inference Framework. CoRR abs/1802.05411 (2018) - [i22]Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu:
Variational Learning on Aggregate Outputs with Gaussian Processes. CoRR abs/1805.08463 (2018) - [i21]Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu, Jun Suzuki, Kentaro Inui:
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions. CoRR abs/1809.00800 (2018) - 2017
- [j33]Momoko Hayamizu, Kenji Fukumizu:
On minimum spanning tree-like metric spaces. Discret. Appl. Math. 226: 51-57 (2017) - [j32]Tomoharu Iwata, Motonobu Kanagawa, Tsutomu Hirao, Kenji Fukumizu:
Unsupervised group matching with application to cross-lingual topic matching without alignment information. Data Min. Knowl. Discov. 31(2): 350-370 (2017) - [j31]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf:
Kernel Mean Embedding of Distributions: A Review and Beyond. Found. Trends Mach. Learn. 10(1-2): 1-141 (2017) - [j30]Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar:
Density Estimation in Infinite Dimensional Exponential Families. J. Mach. Learn. Res. 18: 57:1-57:59 (2017) - [j29]Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka:
Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor. J. Mach. Learn. Res. 18: 189:1-189:41 (2017) - [j28]Momoko Hayamizu, Hiroshi Endo, Kenji Fukumizu:
A Characterization of Minimum Spanning Tree-Like Metric Spaces. IEEE ACM Trans. Comput. Biol. Bioinform. 14(2): 468-471 (2017) - [c43]Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton:
A Linear-Time Kernel Goodness-of-Fit Test. NIPS 2017: 262-271 - [c42]Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu:
Trimmed Density Ratio Estimation. NIPS 2017: 4518-4528 - [i20]Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton:
A Linear-Time Kernel Goodness-of-Fit Test. CoRR abs/1705.07673 (2017) - [i19]Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu:
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. CoRR abs/1709.00147 (2017) - 2016
- [j27]Krikamol Muandet, Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Shrinkage Estimators. J. Mach. Learn. Res. 17: 48:1-48:41 (2016) - [j26]Yu Nishiyama, Kenji Fukumizu:
Characteristic Kernels and Infinitely Divisible Distributions. J. Mach. Learn. Res. 17: 180:1-180:28 (2016) - [j25]Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu:
Filtering with State-Observation Examples via Kernel Monte Carlo Filter. Neural Comput. 28(2): 382-444 (2016) - [c41]Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu, Milos Radovanovic:
Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness. AAAI 2016: 1659-1665 - [c40]Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu:
Structure Learning of Partitioned Markov Networks. ICML 2016: 439-448 - [c39]Genki Kusano, Yasuaki Hiraoka, Kenji Fukumizu:
Persistence weighted Gaussian kernel for topological data analysis. ICML 2016: 2004-2013 - [c38]Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu:
Convergence guarantees for kernel-based quadrature rules in misspecified settings. NIPS 2016: 3288-3296 - [c37]Song Liu, Kenji Fukumizu:
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective. SDM 2016: 747-755 - [i18]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf:
Kernel Mean Embedding of Distributions: A Review and Beyonds. CoRR abs/1605.09522 (2016) - 2015
- [j24]Somayeh Danafar, Kenji Fukumizu, Faustino Gomez:
Kernel-Based Information Criterion. Comput. Inf. Sci. 8(1): 10-24 (2015) - [j23]Md. Ashad Alam, Kenji Fukumizu:
Higher-Order Regularized Kernel Canonical Correlation Analysis. Int. J. Pattern Recognit. Artif. Intell. 29(4): 1551005:1-1551005:24 (2015) - [j22]Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Stefan Harmeling, Jonas Peters:
Computing functions of random variables via reproducing kernel Hilbert space representations. Stat. Comput. 25(4): 755-766 (2015) - [c36]Kazuo Hara, Ikumi Suzuki, Masashi Shimbo, Kei Kobayashi, Kenji Fukumizu, Milos Radovanovic:
Localized Centering: Reducing Hubness in Large-Sample Data. AAAI 2015: 2645-2651 - [c35]Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu:
Reducing Hubness: A Cause of Vulnerability in Recommender Systems. SIGIR 2015: 815-818 - [c34]Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu, Milos Radovanovic:
Reducing Hubness for Kernel Regression. SISAP 2015: 339-344 - [i17]Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters:
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. CoRR abs/1501.06794 (2015) - [i16]Song Liu, Kenji Fukumizu:
Lazy Transfer Learning. CoRR abs/1506.02784 (2015) - [i15]Momoko Hayamizu, Hiroshi Endo, Kenji Fukumizu:
A characterization of minimum spanning tree-like metric spaces. CoRR abs/1510.09155 (2015) - 2014
- [j21]Md. Ashad Alam, Kenji Fukumizu:
Hyperparameter Selection in Kernel Principal Component Analysis. J. Comput. Sci. 10(7): 1139-1150 (2014) - [c33]Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu:
Monte Carlo Filtering Using Kernel Embedding of Distributions. AAAI 2014: 1897-1903 - [c32]Motonobu Kanagawa, Kenji Fukumizu:
Recovering Distributions from Gaussian RKHS Embeddings. AISTATS 2014: 457-465 - [c31]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Estimation and Stein Effect. ICML 2014: 10-18 - [i14]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Shrinkage Estimators. CoRR abs/1405.5505 (2014) - [i13]Pierre Baldi, Kenji Fukumizu, Tomaso A. Poggio:
Deep Learning: Theory, Algorithms, and Applications (NII Shonan Meeting 2014-5). NII Shonan Meet. Rep. 2014 (2014) - 2013
- [j20]Kenji Fukumizu, Le Song, Arthur Gretton:
Kernel Bayes' rule: Bayesian inference with positive definite kernels. J. Mach. Learn. Res. 14(1): 3753-3783 (2013) - [j19]Klaus-Robert Müller, Tülay Adali, Kenji Fukumizu, José C. Príncipe, Sergios Theodoridis:
Special Issue on Advances in Kernel-Based Learning for Signal Processing [From the Guest Editors]. IEEE Signal Process. Mag. 30(4): 14-15 (2013) - [j18]Le Song, Kenji Fukumizu, Arthur Gretton:
Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models. IEEE Signal Process. Mag. 30(4): 98-111 (2013) - [c30]Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, Marco Saerens, Kenji Fukumizu:
Centering Similarity Measures to Reduce Hubs. EMNLP 2013: 613-623 - [c29]Md. Ashad Alam, Kenji Fukumizu:
Higher-Order Regularized Kernel CCA. ICMLA (1) 2013: 374-377 - [i12]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Estimation and Stein's Effect. CoRR abs/1306.0842 (2013) - 2012
- [c28]Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu:
Hypothesis testing using pairwise distances and associated kernels. ICML 2012 - [c27]Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Bernhard Schölkopf:
Learning from Distributions via Support Measure Machines. NIPS 2012: 10-18 - [c26]Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu:
Optimal kernel choice for large-scale two-sample tests. NIPS 2012: 1214-1222 - [c25]Kenji Fukumizu, Chenlei Leng:
Gradient-based kernel method for feature extraction and variable selection. NIPS 2012: 2123-2131 - [c24]Yu Nishiyama, Abdeslam Boularias, Arthur Gretton, Kenji Fukumizu:
Hilbert Space Embeddings of POMDPs. UAI 2012: 644-653 - [i11]Krikamol Muandet, Bernhard Schölkopf, Kenji Fukumizu, Francesco Dinuzzo:
Learning from Distributions via Support Measure Machines. CoRR abs/1202.6504 (2012) - [i10]Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu:
Hypothesis testing using pairwise distances and associated kernels (with Appendix). CoRR abs/1205.0411 (2012) - [i9]Dino Sejdinovic, Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu:
Equivalence of distance-based and RKHS-based statistics in hypothesis testing. CoRR abs/1207.6076 (2012) - [i8]Yu Nishiyama, Abdeslam Boularias, Arthur Gretton, Kenji Fukumizu:
Hilbert Space Embeddings of POMDPs. CoRR abs/1210.4887 (2012) - 2011
- [j17]Yusuke Watanabe, Kenji Fukumizu:
New Graph Polynomials from the Bethe Approximation of the Ising Partition Function. Comb. Probab. Comput. 20(2): 299-320 (2011) - [j16]Yuichi Shiraishi, Kenji Fukumizu:
Statistical approaches to combining binary classifiers for multi-class classification. Neurocomputing 74(5): 680-688 (2011) - [j15]Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R. G. Lanckriet:
Universality, Characteristic Kernels and RKHS Embedding of Measures. J. Mach. Learn. Res. 12: 2389-2410 (2011) - [c23]Kenji Fukumizu, Le Song, Arthur Gretton:
Kernel Bayes' Rule. NIPS 2011: 1737-1745 - [c22]Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R. G. Lanckriet:
Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint. NIPS 2011: 1773-1781 - [c21]Francesco Dinuzzo, Kenji Fukumizu:
Learning low-rank output kernels. ACML 2011: 181-196 - [i7]Yusuke Watanabe, Kenji Fukumizu:
Loopy Belief Propagation, Bethe Free Energy and Graph Zeta Function. CoRR abs/1103.0605 (2011) - [i6]Kenji Fukumizu, Chenlei Leng:
Gradient-based kernel dimension reduction for supervised learning. CoRR abs/1109.0455 (2011) - 2010
- [j14]Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R. G. Lanckriet:
Hilbert Space Embeddings and Metrics on Probability Measures. J. Mach. Learn. Res. 11: 1517-1561 (2010) - [j13]Md. Ashad Alam, Mohammed Nasser, Kenji Fukumizu:
A Comparative Study of Kernel and Robust Canonical Correlation Analysis. J. Multim. 5(1): 3-11 (2010) - [c20]Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, Gert R. G. Lanckriet:
Non-parametric estimation of integral probability metrics. ISIT 2010: 1428-1432 - [c19]Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R. G. Lanckriet:
On the relation between universality, characteristic kernels and RKHS embedding of measures. AISTATS 2010: 773-780 - [i5]Yusuke Watanabe, Kenji Fukumizu:
Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation. CoRR abs/1002.3307 (2010)
2000 – 2009
- 2009
- [c18]Le Song, Jonathan Huang, Alexander J. Smola, Kenji Fukumizu:
Hilbert space embeddings of conditional distributions with applications to dynamical systems. ICML 2009: 961-968 - [c17]Arthur Gretton, Kenji Fukumizu, Zaïd Harchaoui, Bharath K. Sriperumbudur:
A Fast, Consistent Kernel Two-Sample Test. NIPS 2009: 673-681 - [c16]Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Gert R. G. Lanckriet, Bernhard Schölkopf:
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions. NIPS 2009: 1750-1758 - [c15]Yusuke Watanabe, Kenji Fukumizu:
Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation. NIPS 2009: 2017-2025 - [i4]Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Schölkopf:
A note on integral probability metrics and $\phi$-divergences. CoRR abs/0901.2698 (2009) - [i3]Yusuke Watanabe, Kenji Fukumizu:
Graph polynomials and approximation of partition functions with Loopy Belief Propagation. CoRR abs/0903.4527 (2009) - [i2]Yusuke Watanabe, Kenji Fukumizu:
New graph polynomials from the Bethe approximation of the Ising partition function. CoRR abs/0908.3850 (2009) - 2008
- [j12]Katsuyuki Hagiwara, Kenji Fukumizu:
Relation between weight size and degree of over-fitting in neural network regression. Neural Networks 21(1): 48-58 (2008) - [c14]Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Schölkopf:
Injective Hilbert Space Embeddings of Probability Measures. COLT 2008: 111-122 - [c13]Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Characteristic Kernels on Groups and Semigroups. NIPS 2008: 473-480 - 2007
- [j11]Akihiro Tanabe, Kenji Fukumizu, Shigeyuki Oba, Takashi Takenouchi, Shin Ishii:
Parameter estimation for von Mises-Fisher distributions. Comput. Stat. 22(1): 145-157 (2007) - [j10]Kenji Fukumizu, Francis R. Bach, Arthur Gretton:
Statistical Consistency of Kernel Canonical Correlation Analysis. J. Mach. Learn. Res. 8: 361-383 (2007) - [c12]Shotaro Akaho, Kenji Fukumizu:
Active Learning for Network Estimation. CIBCB 2007: 402-409 - [c11]Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf, Kenji Fukumizu:
A kernel-based causal learning algorithm. ICML 2007: 855-862 - [c10]Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf:
Kernel Measures of Conditional Dependence. NIPS 2007: 489-496 - [c9]Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Statistical Test of Independence. NIPS 2007: 585-592 - 2006
- [c8]Marco Cuturi, Kenji Fukumizu:
Kernels on Structured Objects Through Nested Histograms. NIPS 2006: 329-336 - 2005
- [j9]Marco Cuturi, Kenji Fukumizu, Jean-Philippe Vert:
Semigroup Kernels on Measures. J. Mach. Learn. Res. 6: 1169-1198 (2005) - [c7]Kenji Fukumizu, Francis R. Bach, Arthur Gretton:
Statistical Convergence of Kernel CCA. NIPS 2005: 387-394 - [i1]Marco Cuturi, Kenji Fukumizu:
Multiresolution Kernels. CoRR abs/cs/0507033 (2005) - 2004
- [j8]Kenji Fukumizu, Francis R. Bach, Michael I. Jordan:
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces. J. Mach. Learn. Res. 5: 73-99 (2004) - 2003
- [c6]Kenji Fukumizu, Francis R. Bach, Michael I. Jordan:
Kernel Dimensionality Reduction for Supervised Learning. NIPS 2003: 81-88 - 2002
- [c5]Kenji Fukumizu, Shotaro Akaho, Shun-ichi Amari:
Critical Lines in Symmetry of Mixture Models and its Application to Component Splitting. NIPS 2002: 865-872 - 2000
- [j7]Shun-ichi Amari, Hyeyoung Park, Kenji Fukumizu:
Adaptive Method of Realizing Natural Gradient Learning for Multilayer Perceptrons. Neural Comput. 12(6): 1399-1409 (2000) - [j6]Kenji Fukumizu, Shun-ichi Amari:
Local minima and plateaus in hierarchical structures of multilayer perceptrons. Neural Networks 13(3): 317-327 (2000) - [j5]Hyeyoung Park, Shun-ichi Amari, Kenji Fukumizu:
Adaptive natural gradient learning algorithms for various stochastic models. Neural Networks 13(7): 755-764 (2000) - [j4]Kenji Fukumizu:
Statistical active learning in multilayer perceptrons. IEEE Trans. Neural Networks Learn. Syst. 11(1): 17-26 (2000) - [c4]Hyeyoung Park, Kenji Fukumizu, Shun-ichi Amari, Yillbyung Lee:
An Efficient Learning Algorithm Using Naturla Gradient and Second Order Information of Error Surface. PRICAI 2000: 199-207
1990 – 1999
- 1999
- [c3]Kenji Fukumizu:
Generalization Error of Limear Neural Networks in Unidentifiable Cases. ALT 1999: 51-62 - 1998
- [c2]Kenji Fukumizu:
Effect of Batch Learning in Multilayer Neural Networks. ICONIP 1998: 67-70 - [p1]Sumio Watanabe, Kenji Fukumizu:
Probabilistic design. Algorithms and Architectures 1998: 181-229 - 1996
- [j3]Shin Ishii, Kenji Fukumizu, Sumio Watanabe:
A network of chaotic elements for information processing. Neural Networks 9(1): 25-40 (1996) - [j2]Kenji Fukumizu:
A Regularity Condition of the Information Matrix of a Multilayer Perceptron Network. Neural Networks 9(5): 871-879 (1996) - 1995
- [j1]Sumio Watanabe, Kenji Fukumizu:
Probabilistic design of layered neural networks based on their unified framework. IEEE Trans. Neural Networks 6(3): 691-702 (1995) - [c1]Kenji Fukumizu:
Active Learning in Multilayer Perceptrons. NIPS 1995: 295-301
Coauthor Index
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